This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Small, non-coding RNAs (sRNAs) are a relatively recently discovered class of genes that bacteria use to regulate the expression of other genes in response to changing environmental conditions. sRNAs function by base pairing to their mRNA targets and mediating either positive or negative regulatory outcomes. However, sRNA genes in bacteria are difficult to identify, since they are relatively small genes and they do not contain the protein-coding signals that demarcate protein-coding genes. We are using a computational approach that integrates multiple indices to predict sRNA genes in the bacterium Shewanella oneidensis. S. oneidensis is a member of a class of bacteria known as the metal-reducing bacteria. When grown under anaerobic conditions, S. oneidensis can metabolize soluble, toxic heavy metals into insoluble forms. Thus, it is of interest to explore the mechanisms that control this potentially bioremediative function. Our preliminary data suggest that our predictions will be very useful in identifying sRNAs, including potential regulators of anaerobic metal-reducing metabolism. This proposal describes experiments designed to identify and characterize sRNA genes in S. oneidensis, particularly those sRNAs that may be involved in regulating heavy metal metabolism. We are using sRNA gain-of-function and loss-of-function reagents to determine how sRNAs regulate downstream metabolic functions and to identify the mRNA targets of sRNAs. Our analyses should provide novel insights into how sRNA genes are defined, how sRNAs interact with their targets, and how S. oneidensis heavy metal metabolism is controlled.